When predicting the future dynamic response in a power system based on data, it is critical to preserve the spatial and temporal features of the time series data to ensure accuracy. Gated Recurrent Units (GRUs), a variant of RNN, enable learning the spatial and temporal features of time series data [1]}. Thus, the developed TSA approach leverages GRU to construct the generator and the discriminator.
